Can I Run GLM-5 / GLM-5.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 maja 2026
No
No — GLM-5 / GLM-5.1 does not fit in RTX 4090 Laptop (16 GB VRAM, 32 GB RAM). Even at Q2_K it needs about 250.6 GB against 16 GB usable, a shortfall of roughly 234.6 GB.
Confidence: medium
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RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 576 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
GLM-5 / GLM-5.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 1494 GB | ✗ No | — | — | 1488 GB |
| Q8_0 | 796.5 GB | ✗ No | — | — | 790.5 GB |
| Q6_K | 616.1 GB | ✗ No | — | — | 610.1 GB |
| Q5_K_M | 533.3 GB | ✗ No | — | — | 527.3 GB |
| Q4_K_M | 455.2 GB | ✗ No | — | — | 449.2 GB |
| Q3_K_M | 323.2 GB | ✗ No | — | — | 317.1 GB |
| Q2_K | 250.6 GB | ✗ No | — | — | 244.6 GB |
What to watch out for
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 4090 Laptop GPU at 576 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run GLM-5 / GLM-5.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
No — GLM-5 / GLM-5.1 does not fit in RTX 4090 Laptop (16 GB VRAM, 32 GB RAM). Even at Q2_K it needs about 250.6 GB against 16 GB usable, a shortfall of roughly 234.6 GB.
What limits GLM-5 / GLM-5.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Memory. RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) provides about 16 GB usable, and the smallest quantization of this model needs 250.6 GB.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
- GLM-5 / GLM-5.1 on MacBook Pro M4 Max 128 GB
- GLM-5 / GLM-5.1 on MacBook Pro M4 Max 48 GB
- GLM-5 / GLM-5.1 on MacBook Pro M4 Pro 24 GB
- GLM-5 / GLM-5.1 on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- GPT-OSS on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Granite 3.0 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.0 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
GLM-5 / GLM-5.1 on GPUs
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5090
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5070
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060 Ti 8GB
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for coding
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant
Model & Tools
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